Abstract
A reliability evaluation model of distribution network based on improved Elman feedback dynamic neural network is proposed. A self-feedback connection gain coefficient is added to the receiving layer of Elman neural network to measure the influence of historical information on the future state. In addition, the relevant parameters of Elman neural network are optimized by Mean Impact Value(MIV) and Mind Evolutionary Algorithm(MEA). Before the evaluation, the input variables of neural network are preprocessed by grey relational analysis. Taking several typical cable connection modes of typical medium voltage distribution networks as examples, the reliability evaluation is carried out by using the above methods. The evaluation results showes that the average relative error of the proposed method decreases from 5.24e-4 to 3.238e-5 compared with the general neural network evaluation model. It is shown that this method can effectively improve the reliability evaluation accuracy of medium voltage distribution network.
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